An important problem in statistics is the study of spatio-temporal data taking into account the effect of explanatory variables such as latitude, longitude and time. In this paper, a new Bayesian approach for analyzing spatial longitudinal data is proposed. It takes into account linear time regression structures on the mean and linear regression structures on the variance-covariance matrix of normal observations. The spatial structure is included in the time regression parameters and also in the regression structure of the variance covariance matrix. Initially, we present a summary of the spatial models and the Bayesian methodology used to fit the models, as a extension of the longitudinal data analysis. Next, the general spatial temporal model is proposed. Finally, this proposal is used to study rainfall data.